Instructions to use MU-NLPC/CzeGPT-2_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MU-NLPC/CzeGPT-2_summarizer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MU-NLPC/CzeGPT-2_summarizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/CzeGPT-2_summarizer")
model = AutoModelForCausalLM.from_pretrained("MU-NLPC/CzeGPT-2_summarizer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MU-NLPC/CzeGPT-2_summarizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MU-NLPC/CzeGPT-2_summarizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_summarizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MU-NLPC/CzeGPT-2_summarizer

SGLang

How to use MU-NLPC/CzeGPT-2_summarizer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MU-NLPC/CzeGPT-2_summarizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_summarizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MU-NLPC/CzeGPT-2_summarizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_summarizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MU-NLPC/CzeGPT-2_summarizer with Docker Model Runner:
```
docker model run hf.co/MU-NLPC/CzeGPT-2_summarizer
```

hajekad commited on Apr 4, 2022

Commit

b5c41b5

1 Parent(s): 45d5461

Update README.md

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@@ -17,7 +17,7 @@ Along, we also provide a Czech trained tokenizer (vocab and merges) with vocab s
 The model was evaluated on the *test* and *ood-test* partitions of the SumeCzech dataset and compared to the best summarizers yet evaluated on this benchmark (the results taken from <a href="https://ufal.mff.cuni.cz/sumeczech">here</a>).
 The abstract generator yields three sentences that roughly correspond to 40 token average length of abstracts in the SumeCzech. This length of summary was also confirmed by tuning on the validation set.
-We manage to reach state-of-the art on most metrics and
 Test set

 The model was evaluated on the *test* and *ood-test* partitions of the SumeCzech dataset and compared to the best summarizers yet evaluated on this benchmark (the results taken from <a href="https://ufal.mff.cuni.cz/sumeczech">here</a>).
 The abstract generator yields three sentences that roughly correspond to 40 token average length of abstracts in the SumeCzech. This length of summary was also confirmed by tuning on the validation set.
+We manage to reach state-of-the art on most standard metrics.
 Test set